Search

In areas like smoking cessation and cancer screening, where the goal is to educate and get people to take the first steps toward behavior change, “tailored messaging” was developed in the 1990s to try to improve on the effectiveness of one-size-fits-all brochures that are often distributed in clinics. So far, however, the techniques of recommender systems (also called collaborative filtering) that I helped to develop, also starting in the early 1990s (recipient of ACM Software Systems Award last year) , don’t seem to have been applied in tailored health messaging. In this post, I’ll explore what has been tried in tailored health messaging, and where the opportunities might be to incorporate the recommender system techniques that are now ubiquitous in commerce and other applications on the Internet.

Tailored health messaging typically works by asking people to complete some kind on intake assessment, consisting of self-report demographic, behavior, and psychological questions. Some data from health records may be mined if it is available. Then, a customized print brochure is generated.

Hawkins et al (cite at bottom) wrote an excellent summary and agenda setting piece on tailored health messaging. They identify three strategies that have been used in generating the tailored messages. The first they call personalization, which is to make the recipient feel that the message was crafted just for them, by addressing them by name or including a photo of them, by explicitly saying that it was crafted for them, or by including contextual information. The second they call feedback, which is to reflect back some information from the assessment, possibly making some inferences, judgments, or comparisons (e.g., “on average, ex-smokers try to quite at least three times before succeeding). The third is content matching, where a large database of potential message content is filtered to exclude irrelevant messages (e.g., the dangers of second-hand smoke for children in the house, when the person has no children) and to include just the right ones. The content-matching may be based on demographic information or on psychological states (e.g., people with low self-efficacy for quitting may get the messages that try to convince them they can do it, while those with low outcome expectancy get the messages that address the costs and benefits of smoking.)

Recommender Systems are now ubiquitous at sites like Amazon and Netflix. They work by aggregating data from many people and using them to select items to recommend (e.g., people who liked this book also liked…) There’s a whole research community focused on recommender systems that is very technically and mathematically sophisticated.

Why haven’t any of those techniques been applied in tailored health messaging yet, especially for implementing the content matching strategy? I think the answer is the coarse granularity of health messaging, and the information generated about effectiveness of those messages. In the traditional setup, there is just one bundle of content or at most a few that are delivered and the system gets no data about recipient responses to individual elements of that bundle. There’s just not enough information available to do sophisticated recommender systems.

So let’s spin out a scenario where recommender system techniques could apply. Suppose someone has signed up for a smoking cessation program and there is an opportunity to send a stream of messages over time, for exposition let’s suppose one a day as part of a daily check-in about number of cigarettes smoked and current psychological state. Now, the system can directly inquire of the user what their subjective rating is of the message (motivating, unmotivating, informative, too didactic, etc.) and the system can gather an implicit rating based on how effective it is (change in number of cigarettes smoked, change in self-reported psychological state). Now the system can start making inferences about which messages are best to present to which user (people who responded well to message X also responded well to Y, but not Z.)

In recent recommender systems research, “context aware” recommenders have been gaining attention (e.g., workshop the past three years at the RecSys conferences). These systems make different recommendations depending on time of day, weather, other items consumed, consumer location, or other people present.

In the scenario above, imagine that people occasionally receive probes that ask them to report their current psychological state (e.g., craving a cigarette or not; mood). This is sometimes called experience sampling or ecological momentary assessment. If, in addition, messages are delivered in response to the assessment, we can think of it as an ecological momentary intervention. Imagine that the recommender system selects message content in a context-sensitive way, conditioning on location, time of day, number of steps walked so far today as automatically collected with a pedometer, or the self-reported mood or other cues collected through the ecological momentary assessment.